CUSTOMER NEEDS ELICITATION FOR PRODUCT CUSTOMIZATION Yue Wang Advisor: Prof. Tseng Advanced...
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CUSTOMER NEEDS ELICITATION FOR
PRODUCT CUSTOMIZATION
Yue WangAdvisor: Prof. Tseng
Advanced Manufacturing InstituteHong Kong University of Science and Technology
Advanced ManufacturingInstitute
Background
2
Customer Needs
(CNs)
Functional Requirements
(FRs)
Design Parameters
(DPs)
Process Variables
(PVs)
Product specification
definition
Product design
Processdesign
CNs are expressed in explicit product specifications.
Axiomatic design:
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Customer needs elicitation should be
Good: predictive, customer insight
Fast: for customers and for designers
Cheap: reduce market research cost
Easy: reduce drudgery and errors
Introduction
3
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Can we find what people want quickly and
inexpensively?
How to avoid confusing customers with too many
products?
Research issues
4
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5
Challenges
Customers are
Impatient to specify a long list of items
Unable to articulate their needs
Unaware of latent needs
Lack of information about available options
Interlocking among attributes
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Research framework
Bayesian network based preferences representation
Adaptive specification definition procedure
Recommendation for customized product
Approach
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Preferences Representation
Uncertainty of the purchasing choices
Customers are heterogeneous
Choice decisions differ under various situations
The context of purchase differs
Dependency among preferences towards different
attributes
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Bayesian network
Preferences Representation
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The important considerations in this phase:
Customers are not patient enough to specify a long
list of items.
The items differ a lot in terms of the amount of
information they can provide.
Specification Definition
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Basic ideas: Present the most informative query item to
customers
The value of information: :the additional information received about
X from getting the value of Y=y.
)|( yYXf
)|()()|( YXHXHyYXfEY
i
ii ppXH log)(
Specification Definition
10
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The solution for f (Blachman, 1968#):
xx yxp
yxpxp
xpyYXHXHyYXf)|(
1log)|(
)(
1log)()|()()|(
# N. M. Blachman, “The amount of information that y gives about X,” IEEE Trans. Inform. Theory, vol. IT-14, no. 1, pp. 27-31, Jan. 1968
Specification Definition
)|()()|( YXHXHyYXfEY
)yY|X(fEmaxarg*Y YY
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Given: Customers preferences information
Determine: Which products should be recommended? In what order to present the recommendations if
more than one recommendations are presented?
Recommendation
12
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Probability of relevance under binary independent assumption:
Probability of relevance considering first order conditional dependency:
Probabilistic relevance computation
13
mxXPxF imi |)(, mxXPxF imi |)(,
i i
i
SRaP
SRaP
SRCP
SRCPCSRP
),0|(
),1|(
),0|(
),1|(),|1(
ii ii
ii aqq
ppCSRP
)1(
)1(log),|1(
i ii
ii
SRaaP
SRaaP
SRCP
SRCPCSRP
),0,|(
),1,|(
),0|(
),1|(),|1(
)(
)(
i
iiiiii
iiiii
ii
iii
ii
ii aatqtq
rprpa
tq
rpa
ppcSRP )()( )1)(1(
)1)(1(log
)1)(1(
)1)(1(log
)1(
)1(log),|1(
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The idea is to rank products by their estimated
probability of relevance with respect to the information
obtained.
Probability ranking principle is optimal, in the sense that
it minimizes the expected loss.
Probability ranking principle
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Schematic framework
15
Select suitable model
Confirm the specifications
Provide tailored product
Configuration database
Knowledge base
Update knowledge base
End product
Update configuration database
Customer Product development team
Generate the most informative query
Specify the item
Present recommendation
Satisfied with the recommendation?
Y
Start
Process flow
Information flow
Low prob. to find the feasible configuration?
N
N
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Precision rate
Recall rate
Evaluation metrics
16
1
n
iip
Pn
n
pP
n
i i 1
),min(1
mn
pR
n
i i
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The recommendation based on probability ranking can
guarantee the highest precision and recall rate.
If customers’ preferences to all the components are
independent and the potential preferences towards all
the alternatives of an attribute are random, the
specification definition method based on the information
gain has the highest precision and recall rate.
Evaluation results
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Parameters setting Result (# of experiments in which the
precision and recall rate are highest/ total #
of experiments)
m ~ Uniform (3, 13) N ~ Uniform (50, 100)
|Ni|~Uniform
9,345/10,000
m ~ Uniform (5, 15) N ~ Uniform (100, 150)
|Ni|~Uniform
9,325/10,000
m~Uniform (5, 15) N ~ Uniform (1000, 2000)
|Ni|~Uniform
9,344/10,000
m ~ Uniform (5, 15) N ~ Uniform (100, 200)
|Ni|~Norm(1, 1)
9,560/10,000
m ~ Uniform (5, 15) N ~ Uniform (100, 200)
|Ni|~Norm(1, 2)
9,603/10,000
m ~ Uniform (5, 15) N ~ Uniform (100, 200)
|Ni|~Norm(1, 0.5)
9,262/10,000
Evaluation results
18
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Evaluation by utility
Preliminaries:
Stochastically dominate:
If , then approach 1 stochastically
dominates approach 2.
mxXPxF imi |)(,
)()( ,2,1 xFxF mm
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The presented method
stochastically dominates other approaches.
is optimal with respect to any nondecreasing utility
function.
Evaluation results
20
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An approach to elicit customers’ preference is presented.
The model can be used to adaptively improve definition of product specification for custom product design.
Based on the model, customized query sequence can be developed to reduce redundant questions.
Product recommendation approach is adopted to further improve the efficiency of custom product design
Summary
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22
Thank you!
Your suggestions & comments are highly appreciated!
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Extension to binary independent assumption
Theorem: A probability distribution of tree dependence Pt(x) is an optimal approximation to P(x) if and only it’s maximum spanning tree. [Chow and Liu, 1968]
),(maxarg )(ijiiC
xxIMST
ji xx ji
jijiji xPxP
xxPxxPxxI
, )()(
),(log),(),(
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Why customized product design
Well calibrated customized product design can integrate customers into design activities Mitigate the side effect of sticky information Better meet customers’ requirements Loyalty can be enhanced. Help identify latent needs guide future product
development
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Lemma 1: Suppose approach 1 proposes n recommendations in a sequence S1=(r11,r12,…r1n). Each recommendation r1i has probability p1i to meet the customer needs. The sequence is arranged such that
. Approach 2 also proposes n recommendations in a sequence S2=(r21,r22,…r2n). These n recommendations may be different from the ones in sequence S1. Similarly, we also have corresponding probability serial and If for all , then X1 stochastically dominates X2 where Xi is an indicator of the number of satisfactory recommendations by using approach i.
nPPP 11211 ...
}1:{ 2 niP i nPPP 22221 ... ii PP 21
ni 1
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Lemma 2: Suppose approach 1 proposes n recommendations in a sequence S1=(r11,r12,…r1n). Each recommendation r1i has probability p1i to meet the customer needs. The sequence is arranged such that . Approach 2 also proposes n recommendations in a sequence S2=(r21,r22,…r2n) which is a permutation of S1=(r11,r12,…r1n). Then the distribution of satisfactory product for approach 1 is identical to approach 2.
nPPP 11211 ...
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Lemma 3: Let U(x) be a nondecreasing utility function where x is the number of satisfactory recommendations. Let Xi be an indicator of the number of satisfactory recommendations by using approach i. If X1 stochastically dominates X2, then the expected utility by adopting approach 1 is greater or equal to that of approach 2, i.e., .
21 XUEXUE
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Evaluationm: the number of attributes
ni: the number of alternatives of the ith attribute
N: the total number of configurations
Pijk: if the jth alternative of the ith component is selected, the probability that the kth configuration is the desired one.
The entropy of the configuration space if the jth alternative of the ith component is selected:
The expected entropy of the configuration space if the ith component is proposed for a customer to specify:
m
iinN
1
N
kijkijk pp
1
log
N
kijkijk
n
jij ppp
j
11
log28
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Background
Competitive and changing market
Shorter product development time
Product variety proliferation
Bigger penalty cost of failing to meet customers’ needs
or catch up customers’ needs changes
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Probability of relevance (including first order conditional dependency):
Parameters setting:
Probabilistic relevance model
31
i ii
ii
SRaaP
SRaaP
SRCP
SRCPCSRP
),0,|(
),1,|(
),0|(
),1|(),|1(
)(
)(
i
iiiiii
iiiii
ii
iii
ii
ii aatqtq
rprpa
tq
rpa
ppcSRP )()( )1)(1(
)1)(1(log
)1)(1(
)1)(1(log
)1(
)1(log),|1(
),0,0|1(
),0,1|1(
)(
)(
SRaaPr
SRaaPt
iii
iii
),0,1|1(
),1,1|1(
)(
)(
SRaaPq
SRaaPp
iii
iii
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tailor product to different needs
how to avoid confusing customers with too many products Can we find what people want quickly and inexpensively how to find out if a customer is interested in a virtual which doesn't exist
reducing inconsistent preferences good: predictive, customer insight: what people buy or how many will people buy
it fast: for them and for us: it should be fast, doesn't cost so many time cheap: reduce market research cost: should be cheat easy reduce drudgery and errors: should be easy for both customers and
designers That's all the questions in marketing science today.
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